COMPLECS: Parallel Computing Concepts

Thursday, May 2, 2024

11:00 AM - 12:30 PM PDT

This event will be held remotely.

All users of advanced cyberinfrastructure, whether they develop their own software or use 3rd party applications, should understand fundamental parallel computing concepts. In this webinar we cover supercomputer architectures, the differences between threads and processes, implementations of parallelism (e.g., OpenMP and MPI), strong and weak scaling, limitations on scalability (Amdahl’s and Gustafson’s Laws) and benchmarking. We also discuss how to choose the appropriate number of compute cores or nodes when running your applications and, when appropriate, the best balance between threads and processes. This webinar does not assume any programming experience and is suited for a wide audience, including current and prospective users of parallel computers, anyone who expects to write a proposal for computer time or those who are simply curious about parallel computing.

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COMPLECS (COMPrehensive Learning for end-users to Effectively utilize CyberinfraStructure) is a new SDSC program where training will cover non-programming skills needed to effectively use supercomputers. Topics include parallel computing concepts, Linux tools and bash scripting, security, batch computing, how to get help, data management and interactive computing. Each session offers 1 hour of instruction followed by a 30-minute Q&A. COMPLECS is supported by NSF award 2320934.

Instructor

Marty Kandes

Computational and Data Science Research Specialist, SDSC

Marty Kandes a Computational and Data Science Research Specialist in the High-Performance Computing User Services Group at SDSC. He currently helps manage user support for Comet — SDSC’s largest supercomputer. Marty obtained his Ph.D. in Computational Science in 2015 from the Computational Science Research Center at San Diego State University, where his research focused on studying quantum systems in rotating frames of reference through the use of numerical simulation. He also holds an M.S. in Physics from San Diego State University and B.S. degrees in both Applied Mathematics and Physics from the University of Michigan, Ann Arbor. His current research interests include problems in Bayesian statistics, combinatorial optimization, nonlinear dynamical systems, and numerical partial differential equations.